Most signals that are recorded from real-world measurements contain some form of noise. In the context of audio signals, the “noise” reflected in a recording is usually the result of ambient noise in the environment in which the recording was made. In an office environment, such noise may result from HVAC systems, distant traffic, the hum of electrical equipment, etc. In a natural environment, such noise may be the result of wind, birds, flowing water, etc. The real-world rarely, if ever, achieves a state of absolute silence.
The digital representation of a signal is referred to herein as “signal data”. For a variety of reasons, it may be desirable to edit the signal data that is produced by recording a signal. For example, in the context of audio signals, it may be desirable to edit the audio recording of a presentation to remove from the recording the sound of a cough during an otherwise quiet period. To remove the cough without changing the relative timing of the audio recording, the segment of the signal data that contains the audio representation of the cough can be replaced with data that represents silence. However, such an edit would introduce a transition from quiet (with ambient noise) to absolute silence, and then from absolute silence back to quiet. Such transitions will sound unnatural to listeners.
For the purpose of illustration, the examples given herein are in the context of edit operations performed on data that represents audio signals. However, the techniques described hereafter are applicable to any situation in which a signal with background noise is edited.
Referring to FIG. 1, it is a block diagram that depicts a user interface 100 of an audio editing program. The user interface 100 visually depicts a recorded signal as a graph of signal amplitude over time. In the illustrated example, two signals are depicted. The two signals correspond to the left and right channels of a two-channel recording.
As evident by the depiction in FIG. 1, the recorded audio includes periods of relatively high amplitude, and periods of relatively low amplitude. The periods of relatively low amplitude are periods in which only background noise was recorded. The periods of relatively high amplitude are periods in which “foreground information” was recorded.
As used herein, the term “foreground information” refers to any information contained in the signal that is not considered background noise. What constitutes the “foreground information” for any given recording may vary from context to context. For example, the foreground information of a recorded speech may be the portion of the signal in which the speaker is actually speaking.
Referring to FIG. 2, it represents the user interface of FIG. 1 after a user has replaced a segment of the recorded signal with data that represents absolute silence. A user may edit the audio in this manner, for example, in an attempt to delete the sound of a cough during a pause in a recorded speech. Due to the insertion of the silent segment, the audio transitions from ambient noise, to silence, back to ambient noise.
As previously mentioned, such transitions sound unnatural to the listener. Specifically, in the case of an audio signal, the blank data is a noticeable discontinuity. It sounds like a “dropout” and draws attention to the edit. It is desirable to allow users to edit signal data without introducing such unnatural transitions.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.